{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:38:44Z","timestamp":1769733524115,"version":"3.49.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index (ANI). Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the possible relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaa049","type":"journal-article","created":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T14:39:42Z","timestamp":1596724782000},"page":"236-250","source":"Crossref","is-referenced-by-count":19,"title":["Machine learning techniques for computer-based decision systems in the operating theatre: application to analgesia delivery"],"prefix":"10.1093","volume":"29","author":[{"given":"Jose M","family":"Gonzalez-Cava","sequence":"first","affiliation":[{"name":"Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Arnay","sequence":"additional","affiliation":[{"name":"Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Albino","family":"Mendez-Perez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana","family":"Le\u00f3n","sequence":"additional","affiliation":[{"name":"Hospital Universitario de Canarias, 38320 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda","family":"Mart\u00edn","sequence":"additional","affiliation":[{"name":"Hospital Universitario de Canarias, 38320 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose A","family":"Reboso","sequence":"additional","affiliation":[{"name":"Hospital Universitario de Canarias, 38320 La Laguna (Tenerife), Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esteban","family":"Jove-Perez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain and Department of Industrial Engineering, Universidade da Coru\u00f1a, 15405 Coru\u00f1a, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose Luis","family":"Calvo-Rolle","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Universidade da Coru\u00f1a, 15405 Coru\u00f1a, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"2021032210352849000_ref1","doi-asserted-by":"crossref","first-page":"134279","DOI":"10.1016\/j.scitotenv.2019.134279","article-title":"Tackling environmental challenges in pollution controls using artificial intelligence: a review","volume":"699","author":"Ye","year":"2020","journal-title":"Science of the Total Environment"},{"key":"2021032210352849000_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2016.10.030","article-title":"Planning for tourism routes using social networks","volume":"69","author":"Cenamor","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"2021032210352849000_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2298\/CSIS150410029G","article-title":"Smart tourist information points by combining agents, semantics and AI techniques","volume":"14","author":"Garrido","year":"2017","journal-title":"Computer Science and Information Systems"},{"key":"2021032210352849000_ref4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/00986445.2019.1578757","article-title":"Artificial intelligence (AI)-based friction factor models for large piping networks","volume":"207","author":"Parveen","year":"2020","journal-title":"Chemical Engineering Communications"},{"key":"2021032210352849000_ref5","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.asoc.2016.01.007","article-title":"Fuzzy FMEA application to improve decision-making process in an emergency department","volume":"43","author":"Chanamool","year":"2016","journal-title":"Applied Soft Computing"},{"key":"2021032210352849000_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-019-0103-3","article-title":"Predicting scheduled hospital attendance with artificial intelligence","volume":"2","author":"Nelson","year":"2019","journal-title":"NPJ Digital Medicine"},{"key":"2021032210352849000_ref7","doi-asserted-by":"crossref","first-page":"146510","DOI":"10.1016\/j.brainres.2019.146510","article-title":"Artificial intelligence and the detection of pediatric concussion using epigenomic analysis","volume":"1726","author":"Bahado-Singh","year":"2020","journal-title":"Brain Research"},{"key":"2021032210352849000_ref8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.artmed.2016.04.005","article-title":"Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic","volume":"69","author":"de Bruin","year":"2016","journal-title":"Artificial Intelligence in Medicine"},{"key":"2021032210352849000_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41433-019-0577-x","article-title":"The impact of artificial intelligence in the diagnosis and management of glaucoma","volume":"34","author":"Mayro","year":"2020","journal-title":"Eye"},{"key":"2021032210352849000_ref10","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s10877-016-9868-y","article-title":"Adaptive fuzzy modeling of the hypnotic process in anesthesia","volume":"31","author":"Marrero","year":"2017","journal-title":"Journal of Clinical Monitoring and Computing"},{"key":"2021032210352849000_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.conengprac.2015.09.009","article-title":"Adaptive fuzzy predictive controller for anesthesia delivery","volume":"46","author":"Mendez","year":"2016","journal-title":"Control Engineering Practice"},{"key":"2021032210352849000_ref12","doi-asserted-by":"crossref","first-page":"9012720","DOI":"10.1155\/2018\/9012720","article-title":"A novel fuzzy algorithm to introduce new variables in the drug supply decision-making process in medicine","volume":"2018","author":"Gonzalez-Cava","year":"2018","journal-title":"Complexity."},{"key":"2021032210352849000_ref13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.artmed.2017.12.005","article-title":"Improving the anesthetic process by a fuzzy rule based medical decision system","volume":"84","author":"Mendez","year":"2018","journal-title":"Artificial Intelligence in Medicine"},{"key":"2021032210352849000_ref14","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1007\/978-3-030-29859-3_41","article-title":"Inferring knowledge from clinical data for anesthesia automation","volume":"11734","author":"Gonzalez-Cava","year":"2019","journal-title":"Lecture Notes in Computer Science"},{"key":"2021032210352849000_ref15","first-page":"159","article-title":"Hybrid model for the ANI index prediction using remifentanil drug and EMG signal","volume":"84","author":"Casteleiro-Roca","year":"2018","journal-title":"Neural Computing and Applications"},{"key":"2021032210352849000_ref16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1093\/jigpal\/jzy032","article-title":"Modelling the hypnotic patient response in general anaesthesia using intelligent models","volume":"27","author":"Jove","year":"2019","journal-title":"Logic Journal of IGPL"},{"key":"2021032210352849000_ref17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.3390\/s17010179","article-title":"Hybrid intelligent system to perform fault detection on BIS sensor during surgeries","volume":"17","author":"Casteleiro-Roca","year":"2017","journal-title":"Sensors"},{"key":"2021032210352849000_ref18","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1213\/ANE.0000000000002383","article-title":"Brain monitoring and the depth of anesthesia: another Goldilocks dilemma","volume":"126","author":"Shander","year":"2018","journal-title":"Anesthesia and Analgesia"},{"key":"2021032210352849000_ref19","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10916-016-0641-z","article-title":"An evaluation of the state of neuromuscular blockade monitoring devices","volume":"40","author":"Hund","year":"2016","journal-title":"Journal of Medical Systems"},{"key":"2021032210352849000_ref20","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.compbiomed.2016.06.007","article-title":"Adaptive pharmacokinetic and pharmacodynamic modelling to predict propofol effect using BIS-guided anesthesia","volume":"75","author":"Mart\u00edn-Mateos","year":"2016","journal-title":"Computers in Biology and Medicine"},{"key":"2021032210352849000_ref21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.bpa.2005.09.002","article-title":"Monitoring analgesia","volume":"20","author":"Guignard","year":"2006","journal-title":"Best Practice & Research. Clinical Anaesthesiology"},{"key":"2021032210352849000_ref22","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1111\/anae.13018","article-title":"Assessing pain objectively: the use of physiological markers","volume":"70","author":"Cowen","year":"2015","journal-title":"Anaesthesia"},{"key":"2021032210352849000_ref23","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1007\/s00101-015-0080-0","article-title":"Monitoring von Schmerz, Nozizeption und Analgesie unter Allgemeinan\u00e4sthesie","volume":"64","author":"von Dincklage","year":"2015","journal-title":"Anaesthesist"},{"key":"2021032210352849000_ref24","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/IEMBS.2010.5625971","volume-title":"2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Logier","year":"2010"},{"key":"2021032210352849000_ref25","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1093\/bja\/aeu411","article-title":"Postoperative pain after laparoscopic cholecystectomy is not reduced by intraoperative analgesia guided by analgesia nociception index (ANI\u00ae) monitoring: a randomized clinical trial","volume":"114","author":"Szental","year":"2015","journal-title":"British Journal of Anaesthesia"},{"key":"2021032210352849000_ref26","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10877-016-9905-x","article-title":"A novel multivariate STeady-state index during general ANesthesia (STAN)","volume":"31","author":"Castro","year":"2017","journal-title":"Journal of Clinical Monitoring and Computing"},{"key":"2021032210352849000_ref27","first-page":"31","article-title":"Bispectral index (BIS) monitoring during propofol-induced sedation and anaesthesia","author":"Singh","year":"1999"},{"key":"2021032210352849000_ref28","doi-asserted-by":"crossref","first-page":"103294","DOI":"10.1016\/j.jbi.2019.103294","article-title":"Neural network-based approaches for biomedical relation classification: a review","volume":"99","author":"Zhang","year":"2019","journal-title":"Journal of Biomedical Informatics"},{"key":"2021032210352849000_ref29","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/978-3-642-28699-5_15","article-title":"Support vector machines in biomedical and biometrical applications","volume":"13","author":"Cyran","year":"2013","journal-title":"Smart Innovations, Systems and Technologies"},{"key":"2021032210352849000_ref30","doi-asserted-by":"crossref","first-page":"218","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: k-nearest neighbors","volume":"4","author":"Zhang","year":"2016","journal-title":"Annals of Translation Medicine"},{"key":"2021032210352849000_ref31","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-011-9272-4","article-title":"Decision trees: a recent overview","volume":"39","author":"Kotsiantis","year":"2013","journal-title":"Artificial Intelligence Review"},{"key":"2021032210352849000_ref32","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1023\/A:1013912006537","article-title":"Logistic regression, AdaBoost and Bregman distances","volume":"48","author":"Collins","year":"2002","journal-title":"Machine Learning"},{"key":"2021032210352849000_ref33","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdisciplinary Reviews. Data Mining Knowledge Discovery"},{"key":"2021032210352849000_ref34","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TMM.2009.2030632","article-title":"Discriminant subspace analysis: an adaptive approach for image classification","volume":"11","author":"Lu","year":"2009","journal-title":"IEEE Transactions on Multimedia"},{"key":"2021032210352849000_ref35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","article-title":"RUSBoost: a hybrid approach to alleviating class imbalance","volume":"40","author":"Seiffert","year":"2010","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems Humans"},{"key":"2021032210352849000_ref36","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.neucom.2014.09.051","article-title":"A deterministic approach to regularized linear discriminant analysis","volume":"151","author":"Sharma","year":"2015","journal-title":"Neurocomputing"},{"key":"2021032210352849000_ref37","doi-asserted-by":"crossref","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"Hosmer","year":"2013","edition":"3rd"},{"key":"2021032210352849000_ref38","doi-asserted-by":"crossref","first-page":"4563","DOI":"10.1088\/0031-9155\/58\/13\/4563","article-title":"Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy","volume":"58","author":"Gueth","year":"2013","journal-title":"Physics in Medicine and Biology"},{"key":"2021032210352849000_ref39","doi-asserted-by":"crossref","first-page":"e0186906","DOI":"10.1371\/journal.pone.0186906","article-title":"Open source machine-learning algorithms for the prediction of optimal cancer drug therapies","volume":"12","author":"Huang","year":"2017","journal-title":"PLoS One"},{"key":"2021032210352849000_ref40","first-page":"623","article-title":"Editorial II: solid as a ROC","volume-title":"British Journal of Anaesthesia","author":"Galley","year":"2004"},{"key":"2021032210352849000_ref41","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.bja.2019.03.024","article-title":"Objective monitoring of nociception: a review of current commercial solutions","volume":"123","author":"Ledowski","year":"2019","journal-title":"British Journal of Anaesthesia"},{"key":"2021032210352849000_ref42","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1093\/bja\/aex061","article-title":"Pain assessment in conscious healthy volunteers: a crossover study evaluating the analgesia\/nociception index","volume":"118","author":"Yan","year":"2017","journal-title":"BJA British Journal of Anaesthesia"},{"key":"2021032210352849000_ref43","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1093\/bja\/aex210","article-title":"Analgesia nociception index for the assessment of pain in critically ill patients: a diagnostic accuracy study","volume":"119","author":"Chanques","year":"2017","journal-title":"British Journal of Anaesthesia"},{"key":"2021032210352849000_ref44","first-page":"480","article-title":"Measurement of the nociceptive balance by analgesia nociception index and surgical pleth index during sevoflurane-remifentanil anesthesia","author":"Gruenewald","year":"2015"},{"key":"2021032210352849000_ref45","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10877-012-9354-0","article-title":"Variations of the analgesia nociception index during general anaesthesia for laparoscopic abdominal surgery","volume":"26","author":"Jeanne","year":"2012","journal-title":"Journal of Clinical Monitoring and Computing"},{"key":"2021032210352849000_ref46","first-page":"57","article-title":"Analgesia nociception index monitoring during supratentorial craniotomy","author":"Kommula","year":"2019"},{"key":"2021032210352849000_ref47","first-page":"35","article-title":"Benefits of intraoperative analgesia guided by the analgesia nociception index (ANI) in bariatric surgery: an unmatched case-control study","author":"Le Gall","year":"2019"},{"key":"2021032210352849000_ref48","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s10877-015-9802-8","article-title":"Prediction of hemodynamic reactivity using dynamic variations of analgesia\/nociception index (delta ANI)","volume":"30","author":"Boselli","year":"2016","journal-title":"Journal of Clinical Monitoring and Computing"}],"container-title":["Logic Journal of the IGPL"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jigpal\/article-pdf\/29\/2\/236\/36646483\/jzaa049.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jigpal\/article-pdf\/29\/2\/236\/36646483\/jzaa049.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T10:36:10Z","timestamp":1616409370000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jigpal\/article\/29\/2\/236\/5905179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,14]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,9,14]]},"published-print":{"date-parts":[[2021,3,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jigpal\/jzaa049","relation":{},"ISSN":["1367-0751","1368-9894"],"issn-type":[{"value":"1367-0751","type":"print"},{"value":"1368-9894","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,4]]},"published":{"date-parts":[[2020,9,14]]}}}